A data-driven framework for identifying important components in complex systems. (December 2020) - Lu, Xuefei; Baraldi, Piero; Zio, Enrico
Highlights: We propose a data-driven framework to identify critical components of a CTI. The method uses the large amount of monitoring signals collected from the CTI. The method is built on a random forest-based feature selection technique. The method is validated on a nuclear power plant system and a synthetic case study. Abstract: Complex technical infrastructures are systems of systems characterized by hierarchical structures, made by thousands of mutually interconnected components performing different functions. Given their complexity, it is difficult to derive their functional logic using traditional risk and reliability analysis methods based on engineering knowledge. In this work, we propose to address the problem in an innovative way that makes use of the large amount of data available from monitoring those systems. Specifically, we develop a data-driven framework to identify the critical components of a complex technical infrastructure. The criticality of a component with respect to the safe/failed state of the infrastructure is assessed considering a feature selection technique which employs Random Forest (RF) classification and a feature importance score. The proposed data-driven framework is applied to a nuclear power plant system and a synthetic case study, which mimics the complexity of a technical infrastructure.